@inproceedings{liu-etal-2025-team,
title = "Team {QUST} at {S}em{E}val-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framing",
author = "Liu, Jiyan and
Liu, Youzheng and
Wang, Taihang and
Xu, Xiaoman and
Wang, Yimin and
Jiang, Ye",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.139/",
pages = "1052--1056",
ISBN = "979-8-89176-273-2",
abstract = "This paper introduces the participation of the QUST team in subtask 1 of SemEval-2025 Task 10. We evaluate various large language models (LLMs) based on instruction tuning (IT) on subtask 1. Specifically, we first analyze the data statistics, suggesting that the imbalance of label distribution made it difficult for LLMs to be fine-tuned. Subsequently, a voting mechanism is utilized on the predictions of the top-3 models to derive the final submission results. The team participated in all language tracks, achieving 1st place in Hindi (HI), 2nd in Russian (RU), 3rd in Portuguese (PT), 6th in Bulgarian (BG), and 7th in English (EN) on the official test set. We release our system code at: https://github.com/warmth27/SemEval2025{\_}Task10"
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<abstract>This paper introduces the participation of the QUST team in subtask 1 of SemEval-2025 Task 10. We evaluate various large language models (LLMs) based on instruction tuning (IT) on subtask 1. Specifically, we first analyze the data statistics, suggesting that the imbalance of label distribution made it difficult for LLMs to be fine-tuned. Subsequently, a voting mechanism is utilized on the predictions of the top-3 models to derive the final submission results. The team participated in all language tracks, achieving 1st place in Hindi (HI), 2nd in Russian (RU), 3rd in Portuguese (PT), 6th in Bulgarian (BG), and 7th in English (EN) on the official test set. We release our system code at: https://github.com/warmth27/SemEval2025_Task10</abstract>
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%0 Conference Proceedings
%T Team QUST at SemEval-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framing
%A Liu, Jiyan
%A Liu, Youzheng
%A Wang, Taihang
%A Xu, Xiaoman
%A Wang, Yimin
%A Jiang, Ye
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F liu-etal-2025-team
%X This paper introduces the participation of the QUST team in subtask 1 of SemEval-2025 Task 10. We evaluate various large language models (LLMs) based on instruction tuning (IT) on subtask 1. Specifically, we first analyze the data statistics, suggesting that the imbalance of label distribution made it difficult for LLMs to be fine-tuned. Subsequently, a voting mechanism is utilized on the predictions of the top-3 models to derive the final submission results. The team participated in all language tracks, achieving 1st place in Hindi (HI), 2nd in Russian (RU), 3rd in Portuguese (PT), 6th in Bulgarian (BG), and 7th in English (EN) on the official test set. We release our system code at: https://github.com/warmth27/SemEval2025_Task10
%U https://aclanthology.org/2025.semeval-1.139/
%P 1052-1056
Markdown (Informal)
[Team QUST at SemEval-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framing](https://aclanthology.org/2025.semeval-1.139/) (Liu et al., SemEval 2025)
ACL